AI prompts for marketers
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Marketing AI fails as an idea machine and shines as a structured-work machine: mining real audience language, forcing concept variety through named levers, turning metric dumps into readable readouts, and drafting the plan skeletons you refine. The five templates below follow that split.
The recurring mechanic is fact density: campaigns built on verbatim customer phrases and concrete numbers survive contact with reality; campaigns built on adjectives don't.
How to prompt as a marketer
- Mine before you make: feed reviews, tickets and community posts in first — concepts built on harvested language outperform invented insights.
- One audience per prompt: "millennials and Gen Z professionals" produces mush; "first-time managers, 6 months in, drowning in meetings" produces copy.
- Numbers in, numbers out: budgets, benchmarks, current CTRs. Demand quantified claims back and mark the unsupported ones.
- Ban the marketing slop explicitly: "elevate", "unlock", "supercharge" — or every draft needs the same edit.
The five templates
Before any campaign: what does this audience actually complain about, in their own words?
Below is raw audience material about [CATEGORY/PRODUCT]: reviews, support tickets, forum threads, social comments. Extract and deliver: 1) Top 7 pains, each with 2 verbatim quotes (exact wording, no cleanup). 2) Top 5 desired outcomes — their words, not marketing terms. 3) The emotional undertone per pain (frustration / anxiety / resignation…). 4) 5 message hypotheses, each pairing one pain quote with one outcome quote. Do not invent anything not present in the material. If a category is thin, say so. Material: [PASTE 30–200 SNIPPETS]
You need genuinely different campaign directions to react to — not five flavors of the same safe idea.
Product: [PRODUCT]. Audience: [SPECIFIC SEGMENT]. Core fact: [THE ONE QUANTIFIED BENEFIT]. Budget class: [E.G. SMALL/SOCIAL-ONLY]. Brand register: [3 ADJECTIVES + TABOOS]. Generate 6 campaign concepts, one per lever: 1) Magnify — dramatize the core fact to its extreme. 2) Reverse — flip an industry cliché on its head. 3) Substitute — tell it as a customer's story, no product claims. 4) Combine — merge the benefit with the biggest objection. 5) Minify — the 3-word version. 6) Adapt — borrow a mechanic from another industry: [OPTIONAL: NAME ONE]. Per concept: name, one-line big idea, hero execution, first headline. The core fact must appear in at least 4 concepts.
Concept chosen. Now the boring-but-critical part: what runs where, when, with what message depth.
You are my [performance marketing lead]. Build a channel plan skeleton for the campaign below. Campaign: [CONCEPT ONE-LINER]. Objective + KPI: [E.G. 500 SIGNUPS, CPL < €X]. Budget: [TOTAL + SPLIT CONSTRAINTS]. Duration: [WEEKS]. Audience lives on: [WHAT YOU KNOW]. Format: table — channel, role in funnel, message angle (from the concept), format specs, budget %, primary metric, kill criterion (when we stop spending). Then: 3 assumptions this plan makes that we should validate in week 1, each with the cheapest validation test. Ask me up to 3 questions before building if anything is missing.
Month's over, dashboard is full, stakeholders want to know what happened — in one page, in sentences.
Turn the metrics below into a performance readout for [AUDIENCE, E.G. CMO / CLIENT]. Format: 1) Three-sentence summary: what happened, why, what we're changing. 2) Wins and losses — max 3 each, every claim with its number and comparison base (vs. last period / vs. target). 3) One "honest paragraph": what we don't understand yet. 4) Next month's 3 changes, each tied to a finding above. Rules: no metric without a comparison, no "strong performance" without the number, mark anything the data doesn't support as [HYPOTHESIS]. Data: [PASTE METRICS TABLE / EXPORT] Context: [WHAT CHANGED THIS PERIOD — LAUNCHES, BUDGET SHIFTS, SEASONALITY]
One landing page or ad set, and you want a prioritized month of tests instead of random tweaks.
Here is our current [LANDING PAGE COPY / AD SET] and its numbers: [PASTE ASSET + CURRENT CTR/CVR/CPL].
Generate a test backlog: 8 A/B test ideas, each with — hypothesis ("changing X to Y will improve Z because…"), the mechanic behind it (clarity / proof / urgency / friction / specificity), effort (S/M/L), and expected signal strength given our traffic of [SESSIONS OR IMPRESSIONS / WEEK].
Prioritize into: run now (2), run next (3), park (3) — justify the top 2 in one line each. Base every hypothesis on something visible in the asset or numbers, not general best practice.
Frequently asked questions
Can AI replace our audience research?
It can process research you collect — reviews, tickets, transcripts — into patterns and verbatim language (that's the mining template). It cannot know your audience without that input; unfed, it generates plausible-sounding personas that are fiction with confidence.
How do I keep AI campaign copy on-brand?
A standing register block (3 adjectives, taboo words, CTA style) pasted into every session, plus 2–3 approved pieces as calibration examples. The copywriter page has a dedicated register-lock template that marketers can reuse as-is.
Which marketing numbers should I trust from AI?
None it produces itself — benchmarks and "typical CTRs" from a model are folklore. Trust the structure it builds around numbers YOU paste. The readout template enforces that: no metric without your comparison base.
Does this work for B2B and long sales cycles?
Yes — the mechanics are channel-agnostic. For B2B, feed sales-call notes and RFP language into the mining template instead of reviews; the pain-quote → outcome-quote pairing works identically for buying committees.